Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice

Analyzing the Adoption, Cropping Rotation, and Impact of Winter Cover Crops in the Mississippi Alluvial Plain (MAP) Region through Remote Sensing Technologies

This dissertation explores the application of remote sensing technologies in conservation agriculture, specifically focusing on identifying and mapping winter cover crops and assessing voluntary cover crop adoption and cropping patterns in the Arkansas portion of the Mississippi Alluvial Plain (MAP). In the first chapter, a systematic review using the PRISMA methodology examines the last 30 years of thematic research, development, and trends in remote sensing applied to conservation agriculture from a global perspective. The review uncovers a growing interest in remote sensing-based research in conservation agriculture and emphasizes the necessity for further studies dedicated to conservation practices. Among the 68 articles examined, 94% of studies utilized a pixel-based classification method, while only 6% employed an object-based approach. The analysis also revealed a thematic shift over time, with tillage practices being extensively studied before 2005, followed by a focus on crop residue from 2004 to 2012. From 2012 to 2020, there was a renewed emphasis on cover crops research. These findings highlight the evolving research landscape and provide insights into the trends within remote sensing-based conservation agriculture studies. The second chapter presents a methodological framework for identifying and mapping winter cover crops. The framework utilizes the Google Earth Engine (GEE) and a Random Forest (RF) classifier with time series data from Landsat 8 satellite. Results demonstrate a high classification accuracy (97.7%) and a significant increase (34%) in model-predicted cover crop adoption over the study period between 2013 and 2019. Additionally, the study showcases the use of multi-year datasets to efficiently map the growing season\u27s length and cover crops\u27 phenological characteristics. The third chapter assesses the voluntary adoption of winter cover crops and cropping patterns in the MAP region. Remote sensing technologies, USDA-NRCS government cover crop data sources, and the USDA Cropland Data Layer (CDL) are employed to identify cover crop locations, analyze county-wide voluntary adoption, and cropping rotations. The result showed a 5.33% increase in the overall voluntary adoption of cover crops in the study region between 2013 and 2019. The findings also indicate a growing trend in cover crop adoption, with soybean-cover crop rotations being prominent. This dissertation enhances our understanding of the role of remote sensing in conservation agriculture with a particular focus on winter cover crops. These insights are valuable for policymakers, stakeholders, and researchers seeking to promote sustainable agricultural practices and increased cover crop adoption. The study also underscores the significance of integrating remote sensing technologies into agricultural decision-making processes and highlights the importance of collaboration among policymakers, researchers, and producers. By leveraging the capabilities of remote sensing, it will enhance conservation agriculture contribution to long-term environmental sustainability and agricultural resilience. Keywords: Remote sensing technologies, Conservation agriculture, Winter cover crops, Voluntary adoption, Cropping patterns, Sustainable agricultural practice

Study on secondary flow losses modellig in compressor cascades using Reynolds averaged Navier-Stokes techniques

The present study explores the feasibility of using the GEKO turbulence model in Ansys Fluent software to analyze secondary flow losses in turbomachinery compressors. This turbulence model includes free parameters that can be adjusted to enhance simulation accuracy. The model was calibrated to match reference data obtained from a Large Eddy Simulation (LES), by focusing on two dimensional simulations at the midspan of the compressor blade. Subsequently, the obtained calibrated values were used to perform three dimensional simulations. The calibrated GEKO model showed similar results to the default GEKO model, indicating limited improvement. However, both GEKO models outperformed the standard k − ω model in predicting pressure losses. Vortical structures, such as the tip leakage vortex, were captured by all simulations. The positioning of the vortex in the GEKO simulations was consistent with the reference data. However, the k −ω model showed displacements with respect to the LES simulation. These findings highlight the potential of the GEKO model for practical applications in turbomachinery compressor analysis

Assessment of heavy metals concentration in water and Tengra fish (Mystus vittatus) of Surma River in Sylhet region of Bangladesh

The study was carried out to assess the concentration of heavy metals in water and Tengra fish (Mystus vittatus) of the Surma River, the largest water basin ecosystem covering the north-eastern parts of Bangladesh. Water and Tengra fish (M. vittatus) samples were collected from a total of six sampling stations in which three sampling stations were in Sylhet district and the rest three were in Sunamganj district. Samples were collected from February 2017 to June 2017 on a monthly basis. Water and Tengra fish (M. vittatus) samples were analyzed for the detection of heavy metals viz., lead (Pb), chromium (Cr) and cadmium (Cd) concentrations. Atomic absorption spectrophotometry was used for the detection of heavy metals after digestion of the samples. Pb and Cr were detected from both water and Tengra fish (M. vittatus) samples collected from all the six sampling stations of Sylhet and Sunamganj district. But, Cd was not found both in water and Tengra fish (M. vittatus) during the study period. This study concluded that the detected concentrations of metals (Pb and Cr) in the studied Tengra fish (M. vittatus) muscles were accepted by the international legislation limits and are safe for human consumption. But in water, Pb is the only metal that potentially poses the ecological risk to the water body as it exceeds the acceptance level recommended by World Health Organization (WHO). Consequently, close monitoring of metals pollution of the Surma River is recommended with a view to minimizing the health risk of the population that depend on the river for their water and fish supply

Footwear and insole design parameters to prevent occurrence and recurrence of neuropathic plantar forefoot ulcers in patients with diabetes:a series of N-of-1 trial study protocol

BACKGROUND: Foot complications occur in conjunction with poorly controlled diabetes. Plantar forefoot ulceration contributes to partial amputation in unstable diabetics, and the risk increases with concomitant neuropathy. Reducing peak plantar forefoot pressure reduces ulcer occurrence and recurrence. Footwear and insoles are used to offload the neuropathic foot, but the success of offloading is dependent on patient adherence. This study aims to determine which design and modification features of footwear and insoles improve forefoot plantar pressure offloading and adherence in people with diabetes and neuropathy. METHODS: This study, involving a series of N-of-1 trials, included 21 participants who had a history of neuropathic plantar forefoot ulcers. Participants were recruited from two public hospitals and one private podiatry clinic in Sydney, New South Wales, Australia. This trial is non-randomised and unblinded. Participants will be recruited from three sites, including two high-risk foot services and a private podiatry clinic in Sydney, Australia. Mobilemat™ and F-Scan® plantar pressure mapping systems by TekScan® (Boston, USA) will be used to measure barefoot and in-shoe plantar pressures. Participants’ self-reports will be used to quantify the wearing period over a certain period of between 2 and 4 weeks during the trial. Participant preference toward footwear, insole design and quality-of-life-related information will be collected and analysed. The descriptive and inferential statistical analyses will be performed using IBM SPSS Statistics (version 27). And the software NVivo (version 12) will be utilised for the qualitative data analysis. DISCUSSION: This is the first trial assessing footwear and insole interventions in people with diabetes by using a series of N-of-1 trials. Reporting self-declared wearing periods and participants’ preferences on footwear style and aesthetics are the important approaches for this trial. Patient-centric device designs are the key to therapeutic outcomes, and this study is designed with that strategy in mind. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000699965p. Registered on June 23, 202

In silico identification and characterization of AGO, DCL and RDR gene families and their associated regulatory elements in sweet orange (Citrus sinensis L.).

RNA interference (RNAi) plays key roles in post-transcriptional and chromatin modification levels as well as regulates various eukaryotic gene expressions which are involved in stress responses, development and maintenance of genome integrity during developmental stages. The whole mechanism of RNAi pathway is directly involved with the gene-silencing process by the interaction of Dicer-Like (DCL), Argonaute (AGO) and RNA-dependent RNA polymerase (RDR) gene families and their regulatory elements. However, these RNAi gene families and their sub-cellular locations, functional pathways and regulatory components were not extensively investigated in the case of economically and nutritionally important fruit plant sweet orange (Citrus sinensis L.). Therefore, in silico characterization, gene diversity and regulatory factor analysis of RNA silencing genes in C. sinensis were conducted by using the integrated bioinformatics approaches. Genome-wide comparison analysis based on phylogenetic tree approach detected 4 CsDCL, 8 CsAGO and 4 CsRDR as RNAi candidate genes in C. sinensis corresponding to the RNAi genes of model plant Arabidopsis thaliana. The domain and motif composition and gene structure analyses for all three gene families exhibited almost homogeneity within the same group members. The Gene Ontology enrichment analysis clearly indicated that the predicted genes have direct involvement into the gene-silencing and other important pathways. The key regulatory transcription factors (TFs) MYB, Dof, ERF, NAC, MIKC_MADS, WRKY and bZIP were identified by their interaction network analysis with the predicted genes. The cis-acting regulatory elements associated with the predicted genes were detected as responsive to light, stress and hormone functions. Furthermore, the expressed sequence tag (EST) analysis showed that these RNAi candidate genes were highly expressed in fruit and leaves indicating their organ specific functions. Our genome-wide comparison and integrated bioinformatics analyses provided some necessary information about sweet orange RNA silencing components that would pave a ground for further investigation of functional mechanism of the predicted genes and their regulatory factors